refactor: both rebound compression scatter and histogram use the same…

[wfelliss]

… data.

refactor: added a lower bound to stop noise populating the suspension data

[gemini-code-assist]

Code Review

This pull request refactors suspension telemetry analysis by introducing a centralized filterLowActivityOutliers function in run-analysis.ts to filter out statistical outliers and low-activity events. This function is integrated into both ReboundCompressionPlot and VelocityHistogram to ensure consistent data filtering across graphs. Additionally, unused velocity sampling utilities were removed from telemetryUtils.ts, and the velocity histogram now supports auto-scaling. The review feedback highlights a potential bug where a standard deviation of zero would incorrectly filter out all data points, and suggests implementing a caching mechanism using a WeakMap to optimize the performance of the heavy filtering computations in VelocityHistogram.

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[gemini-code-assist]

If all elements in items have the same velocity or displacement (for example, if there is only one item in the array), the standard deviation (vStd or dStd) will be 0. In this case, the expression Math.abs(a.velocity - vMean) < 5 * vStd evaluates to 0 < 0 (which is false), causing all items to be filtered out. We should handle the case where the standard deviation is 0 to prevent valid data from being incorrectly discarded.

Suggested change

	return items.filter((a) => {
	const passesStdDev =
	Math.abs(a.velocity - vMean) < 5 * vStd &&
	Math.abs(a.displacement - dMean) < 5 * dStd
	// Drop only low-activity points: slow AND small relative to full travel.
	const isLowActivity =
	Math.abs(a.velocity) < minVelocity &&
	Math.abs(a.displacement) < minDisplacement
	return passesStdDev && !isLowActivity
	})
	return items.filter((a) => {
	const passesStdDev =
	(vStd === 0 || Math.abs(a.velocity - vMean) < 5 * vStd) &&
	(dStd === 0 || Math.abs(a.displacement - dMean) < 5 * dStd)
	// Drop only low-activity points: slow AND small relative to full travel.
	const isLowActivity =
	Math.abs(a.velocity) < minVelocity &&
	Math.abs(a.displacement) < minDisplacement
	return passesStdDev && !isLowActivity
	})

[gemini-code-assist]

The buildFilteredVelocities function performs heavy computations (including processCompressions and multiple array filtering/mapping operations) on telemetry data. Since the series prop is often recreated on every render by parent components, useMemo will re-run this function frequently, leading to UI lag. We can introduce a WeakMap cache keyed by the stable rawData array reference to cache the computed velocities and avoid redundant calculations.

const velocityCache = new WeakMap<
  RawSuspensionData[],
  {
    freq: number;
    length: number;
    min: number;
    max: number;
    result: number[];
  }
>();

function buildFilteredVelocities(seriesItem: VelocityHistogramSeries): number[] {
  const length = seriesItem.length ?? 220;
  const min = seriesItem.min ?? 0;
  const max = seriesItem.max ?? 4096;
  const freq = seriesItem.freq;

  const cached = velocityCache.get(seriesItem.rawData);
  if (
    cached &&
    cached.freq === freq &&
    cached.length === length &&
    cached.min === min &&
    cached.max === max
  ) {
    return cached.result;
  }

  const activities = processCompressions(
    seriesItem.rawData,
    freq,
    length,
    min,
    max,
  );

  // Filter compression and rebound subsets separately, exactly as the scatter
  // does, then combine so the histogram bins precisely the scatter's points.
  const kept = [
    ...filterLowActivityOutliers(
      activities.filter((a) => a.type === "compression"),
      length,
    ),
    ...filterLowActivityOutliers(
      activities.filter((a) => a.type === "rebound"),
      length,
    ),
  ];

  const result = kept.map((a) => a.velocity);
  velocityCache.set(seriesItem.rawData, { freq, length, min, max, result });
  return result;
}